The computer processing of forward-look sonar video imagery enables significant capabilities in a wide variety of underwater operations within turbid environments. Accurate automated registration of sonar video images to complement measurements from traditional positioning devices can be instrumental in the detection, localization, and tracking of distinct scene targets, building feature maps, change detection, as well as improving precision in the positioning of unmanned submarines. This work offers a novel solution for the registration of two-dimensional (2-D) forward-look sonar images recorded from a mobile platform, by optimization over the sonar 3-D motion parameters. It incorporates the detection of key features and landmarks, and effectively represents them with Gaussian maps. Improved performance is demonstrated with respect to the state-of-the-art approach utilizing 2-D similarity transformation, based on experiments with real data.

This paper reports the development of a robotic inspection system using a mechanical contact mechanism that enhances the positioning stability of a small and lightweight underwater robot to take clear images of underwater targets and to work with manipulators for inspections under external disturbances. As described in this paper, first we perform a two-dimensional numerical analysis based on force and moment acting on an underwater robot with a contact mechanism. Second, we experimentally investigate the friction coefficients of several soft and high friction materials for the contact points of a prototype contact mechanism to enhance the positioning stability of the robot. Based on the results of numerical analysis and the experimental investigation, we design and develop a prototype contact mechanism for an underwater robot. Moreover, we experimentally test the stability of the underwater robot with the contact mechanism in a test tank. Finally, a ship hull inspection is conducted as a field test in a port using the robot with the developed contact mechanism. The experimentally obtained results indicate that the proposed contact mechanism is a useful tool for underwater visual inspections and manipulator tasks of a small and lightweight underwater robot.

This paper presents terrain mapping and path-planning techniques that are key issues for autonomous mobility of a planetary exploration rover. In this work, a LIDAR (light detection and ranging) sensor is used to obtain geometric information on the terrain. A point cloud of the terrain feature provided from the LIDAR sensor is usually converted to a digital elevation map. A sector-shaped reference grid for the conversion process is proposed in this paper, resulting in an elevation map with cylindrical coordinates termed as C²DEM. This conversion approach achieves a range-dependent resolution for the terrain mapping: a detailed terrain representation near the rover and a sparse representation far from the rover. The path planning utilizes a cost function composed of terrain inclination, terrain roughness, and path length indices, each of which is subject to a weighting factor. The multipath planning developed in this paper first explores possible sets of weighting factors and generates multiple candidate paths. The most feasible path is then determined by a comparative evaluation between the candidate paths. Field experiments with a rover prototype at a Lunar/Martian analog site were performed to confirm the feasibility of the proposed techniques, including the range-dependent terrain mapping with C²DEM and the multipath-planning method.

This paper presents the results from field testing of a unique approach to the navigation of a fleet of autonomous underwater vehicles (AUVs) using only onboard sensors and information provided by a moving surface ship. The approach, considered moving short-baseline (MSBL) navigation, uses two transponders mounted on a single surface ship that alternately broadcast acoustic messages containing one of the parameters of the kinematic state of the surface ship. The broadcasts are initiated according to a predefined schedule so that the one-way travel time (OWTT) of the acoustic messages may be used to determine the range to the transponder. Each AUV in the fleet uses the surface ship state measurements and ranges provided by the acoustic messages in two extended Kalman filters (EKFs) for state estimation. The first EKF merges the intermittent surface ship state measurements with a kinematic model to estimate the state of the surface ship. This is necessary because the presented approach uses 13-bit acoustic messages as opposed to the more commonly used 32-byte messages, which allow the full state to be encoded in a single broadcast. The second EKF uses the current surface ship state estimate to properly interpret the acoustic ranges, combining them with a kinematic model to estimate the state of the AUV itself. Numerous MSBL navigation experiments were compared against a more traditional approach using a long-baseline (LBL) array of transponders and OWTT acoustic ranging. The results of all tests were verified by independent LBL measures of position.

We explore the problem of energy-efficient, time-constrained path planning of a solar-powered robot embedded in a terrestrial environment. Because of the effects of changing weather conditions, as well as sensing concerns in complex environments, a new method for solar power prediction is desirable. We present a method that uses Gaussian Process regression to build a solar map in a data-driven fashion. Using this map and an empirical model for energy consumption, we perform dynamic programming to find energy-minimal paths. We validate our map construction and path-planning algorithms with outdoor experiments, and we perform simulations on our solar maps to further determine the limits of our approach. Our results show that we can effectively construct a solar map using only a simple current measurement circuit and basic GPS localization, and this solar map can be used for energy-efficient navigation. This establishes informed solar harvesting as a viable option for extending system lifetime even in complex environments with low-cost commercial solar panels.

Global localization has long been considered one of the most important but also one of the most challenging localization problems for mobile robots. Current studies of global localization in the literature are mainly based on the Bayesian filtering technique, which can provide an elegant statistical framework for uncertainty management and multisensory fusion. However, the majority of implementations of Bayesian filters for global localization obey the same update rules in such a location-driven sense that they guess the robot location first and then adjust the guess by incorporating the current observation data. This leads to some problematic consequences in that the system suffers from great computational load in a large application area and it cannot recover from localization failure. To overcome the above limitations, this paper deviates from the conventional update rules of Bayes filters and proposes a new approach: the observation-driven Bayes filters (OD-BFs). As the name implies, OD-BFs estimate the robot state just according to the most recent observations and then adjust the estimate by incorporating the dead-reckoning information. We further implement an observation-driven Bayes filter to globally estimate the robot pose in the field area. This global localization system features an effective pose estimation framework that can operate with a large amount of point data in a coarse-to-fine manner. Sufficient experiments were carried out to determine both the advantages and the disadvantages of our OD-BF localization approaches compared with previous ones.

We present an odometry-free three-dimensional (3D) point cloud registration strategy for outdoor environments based on area attributed planar patches. The approach is split into three steps. The first step is to segment each point cloud into planar segments, utilizing a cached-octree region growing algorithm, which does not require the 2.5D image-like structure of organized point clouds. The second step is to calculate the area of each segment based on small local faces inspired by the idea of surface integrals. The third step is to find segment correspondences between overlapping point clouds using a search algorithm, and compute the transformation from determined correspondences. The transformation is searched globally so as to maximize a spherical correlation-like metric by enumerating solutions derived from potential segment correspondences. The novelty of this step is that only the area and plane parameters of each segment are employed, and no prior pose estimation from other sensors is required. Four datasets have been used to evaluate the proposed approach, three of which are publicly available and one that stems from our custom-built platform. Based on these datasets, the following evaluations have been done: segmentation speed benchmarking, segment area calculation accuracy and speed benchmarking, processing data acquired by scanners with different fields of view, comparison with the iterative closest point algorithm, robustness with respect to occlusions and partial observations, and registration accuracy compared to ground truth. Experimental results confirm that the approach offers an alternative to state-of-the-art algorithms in plane-rich environments.

This paper presents a prototype system that enables an autonomous underwater vehicle (AUV) to autonomously track and follow a shark that has been tagged with an acoustic transmitter. The AUV's onboard processor handles both real-time estimation of the shark's two-dimensional planar position, velocity, and orientation states, as well as a straightforward control scheme to drive the AUV toward the shark. The AUV is equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by the acoustic tag. The particular hydrophone system used here provides a measurement of relative bearing angle to the tag, but it does not provide the sign (+ or −) of the bearing angle. Estimation is accomplished using a particle filter that fuses bearing measurements over time to produce a state estimate of the tag location. The particle filter combined with a heuristic-based controller allows the system to overcome the ambiguity in the sign of the bearing angle. The state estimator and control scheme were validated by tracking both a stationary tag and a moving tag with known positions. Offline analysis of these data showed that state estimation can be improved by optimizing diffusion parameters in the prediction step of the filter, and considering signal strength of the acoustic signals in the resampling stage of the filter. These experiments revealed that state estimate errors were on the order of those obtained by current long-distance shark-tracking methods, i.e., manually driven boat-based tracking systems. Final experiments took place in SeaPlane Lagoon, Los Angeles, where a 1-m leopard shark (Triakis semifasciata) was caught, tagged, and released before being autonomously tracked and followed by the proposed AUV system for several hours. © 2013 Wiley Periodicals, Inc.

There are many unanswered questions regarding the construction and purpose of the Great Pyramid of Giza, Egypt. A climbing robot called “Djedi” has been designed, constructed, and deployed to explore shafts of the queen's chamber within the Great Pyramid. The Djedi robot is based on the concept of inchworm motion and is capable of carrying a long reach drill or snake camera. The robot successfully climbed the southern shaft of the Great Pyramid, deployed its snake camera, and revealed writing not seen for thousands of years. This paper details the design of the robot, including climbing steps in the shaft and lessons learned from experimental deployment. © 2013 Wiley Periodicals, Inc.

In recent years, a number operational unmanned ground vehicles (UGVs) have been developed that can negotiate irregular terrain. They have a number of degrees-of-freedom (DOF) giving them enhanced mobility, e.g., the ability to climb stairs and over obstacles. However, operating them remotely is complicated because their controllers are similar to conventional control pads or joysticks used in computer games or toys. It is hard for the operator to achieve an intuitive and natural feel, thus mistakes are common. To intuitively control the locomotion of a UGV with many DOFs, a master-slave operation was implemented. A novel UGV called Kurogane, which consists of a typical crawler combined with a human-like torso section, was developed. The torso section is controlled via a wearable controller interface. In addition, the UGV is equipped with models of muscle viscoelasticity and stretch reflex, called the involuntary autonomous adaptation system, inspired by the adaptive compliance of animals. The proposed system can autonomously and flexibly react and adapt to irregular terrain in real time. Therefore, the operation of Kurogane is simple and does not require great skill or precision. Experimental results show that it performs well over a fixed step, stairs, and rough outdoor terrain. © 2013 Wiley Periodicals, Inc.

This paper describes planar motion modeling for an unmanned surface vehicle (USV), including a comparative evaluation of several experimentally identified models over a wide range of speeds and planing conditions. The modeling and identification objective is to determine a model that is sufficiently rich to enable effective model-based control design and trajectory optimization, sufficiently simple to allow parameter identification, and sufficiently general to describe a variety of hullforms and actuator configurations. We focus, however, on a specific platform: a modified rigid hull inflatable boat with automated throttle and steering. Analysis of experimental results for this vessel indicates that Nomoto's first-order steering model provides the best compromise between simplicity and fidelity at higher speeds. At low speeds, it is helpful to include a first-order lag model for sideslip. Accordingly, we adopt a multiple model approach in which the model structure and parameter values are scheduled based on the nominal forward speed. The speed-scheduled planar motion model may be used to generate dynamically feasible trajectories and to develop trajectory tracking control laws. The paper describes the development, analysis, and experimental implementation of two trajectory tracking control algorithms: a cascade of proportional-derivative controllers and a nonlinear controller obtained through backstepping. Experimental results indicate that the backstepping controller is much more effective at tracking trajectories with highly variable speed and course angle. © 2013 Wiley Periodicals, Inc.

A typical unmanned ground vehicle (UGV) mission can be composed of various tasks and several alternative paths. Small UGVs are typically teleoperated and rely on electric rechargeable batteries for their operations. Since each battery has limited energy storage capacity, it is essential to predict the expected mission energy requirement during the mission execution and update this prediction adaptively via real-time performance measurements (e.g., vehicle power consumption and velocity). We propose and compare two methods in this paper. One is based on recursive least-squares estimation built upon a UGV longitudinal dynamics model. The other is based on Bayesian estimation when prior knowledge (e.g., road average grade and operator driving style) is available. The proposed Bayesian prediction can effectively combine prior knowledge with real-time performance measurements for adaptively updating the prediction of the mission energy requirement. Our experimental and simulation studies show that the Bayesian approach can yield more accurate predictions even with moderately imprecise prior knowledge. © 2013 Wiley Periodicals, Inc.

GPS-denied closed-loop autonomous control of unstable Unmanned Aerial Vehicles (UAVs) such as rotorcraft using information from a monocular camera has been an open problem. Most proposed Vision aided Inertial Navigation Systems (V-INSs) have been too computationally intensive or do not have sufficient integrity for closed-loop flight. We provide an affirmative answer to the question of whether V-INSs can be used to sustain prolonged real-world GPS-denied flight by presenting a V-INS that is validated through autonomous flight-tests over prolonged closed-loop dynamic operation in both indoor and outdoor GPS-denied environments with two rotorcraft unmanned aircraft systems (UASs). The architecture efficiently combines visual feature information from a monocular camera with measurements from inertial sensors. Inertial measurements are used to predict frame-to-frame transition of online selected feature locations, and the difference between predicted and observed feature locations is used to bind in real-time the inertial measurement unit drift, estimate its bias, and account for initial misalignment errors. A novel algorithm to manage a library of features online is presented that can add or remove features based on a measure of relative confidence in each feature location. The resulting V-INS is sufficiently efficient and reliable to enable real-time implementation on resource-constrained aerial vehicles. The presented algorithms are validated on multiple platforms in real-world conditions: through a 16-min flight test, including an autonomous landing, of a 66 kg rotorcraft UAV operating in an unconctrolled outdoor environment without using GPS and through a Micro-UAV operating in a cluttered, unmapped, and gusty indoor environment. © 2013 Wiley Periodicals, Inc.

This paper presents a system enabling robotic helicopters to fly safely without user interaction at low altitude over unknown terrain with static obstacles. The system includes a novel reactive behavior-based method that guides rotorcraft reliably to specified locations in sparsely occupied environments. System dependability is, among other things, achieved by utilizing proven system components in a component-based design and incorporating safety margins and safety modes. Obstacle and terrain detection is based on a vertically mounted off-the-shelf two-dimensional LIDAR system. We introduce two flight modes, pirouette descent and waggle cruise, which extend the field of view of the sensor by yawing the aircraft. The two flight modes ensure that all obstacles above a minimum size are detected in the direction of travel. The proposed system is designed for robotic helicopters with velocity and yaw control inputs and a navigation system that provides position, velocity, and attitude information. It is cost effective and can be easily implemented on a variety of helicopters of different sizes. We provide sufficient detail to facilitate the implementation on single-rotor helicopters with a rotor diameter of approximately 1.8 m. The system was extensively flight-tested in different real-world scenarios in Queensland, Australia. The tests included flights beyond visual range without a backup pilot. Experimental results show that it is feasible to perform dependable autonomous flight using simple but effective methods. © 2013 Wiley Periodicals, Inc.

Deployment of field robots in unstructured environments continues to rise, and the electrochemical battery remains the de facto standard for energy storage in robotic applications. However, robot mission planning, which relies on battery depletion time information, enforces conservative operation due to the lack of statistical rigor on the run-time predictions. A two-tier self-supervised load characterization methodology for mobile robots operating in unstructured environments is proposed. Coupled with load characterization, a model-based statistical battery remaining run-time prediction algorithm utilizing particle filtering is presented. Given measured power loads during operation, the characterization algorithm employs Gaussian mixture modeling to cluster measured loads into a priori unknown regions. With clustered power demand regions, the computation of transition probabilities between the mixture models provides a jump Markov characterization of the historical power loads. An experimental study utilized Packbot data gathered during operation on general desert terrain. A particle filter prediction framework was shown to more accurately predict the remaining run-time of the Packbot given the unstructured terrain compared to existing load-averaging techniques. © 2013 Wiley Periodicals, Inc.